Bibliographic record
Abstract
The governance of research ethics in Canada, including its research ethics boards (REBs), which correspond to the institutional review boards in the U.S., often is portrayed as an exemplary model of cross-disciplinary cooperation and consultation that is altruistically striving to protect research subjects from abuses in biomedical, social sciences, and humanities research. While there is indeed a great deal of altruism and good intention among those involved in this governance, power and interests also play a role that is of particular concern for political scientists. Governance arrangements have been driven by biomedical research, which is vastly better funded than social sciences and humanities (SSH) research. These arrangements have been imposed on the SSH research community with little sensitivity to the distinctive problems of SSH research, despite concerns about such problems that political scientists and other SSH researchers have expressed for a decade. A recent proposal initiated by major research funders to dramatically strengthen research ethics governance has generated even more alarm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.033 | 0.024 |
| Scholarly communication | 0.020 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".